Effect of Two Different Irrigation Strategies on the Yield Components of Five Rice Varieties in a Cold Mediterranean climate in the South-Central Zone of Chile
Bibliographic record
Abstract
Rice is one of the agricultural species that consumes a lot of water. In countries with cold climates such as Chile, a 200 mm high sheet of water is used to cope with night temperatures below 10 °C. This practice consumes 18,000 m3 ha-1 of water per season. Climate change is currently a major problem, where the reduction in precipitation can exceed 40%. Under this context, the current rice production system is unsustainable. The objective of the research seeks to evaluate five rice varieties with different lengths of the productive cycle, under two water management strategies (intermittent vs. traditional irrigation), evaluating the yield components of rice varieties. The trial was conducted in the Mediterranean climate of South-Central Chile, zone is characterized by low temperatures. The traditional commercial variety used was Zafiro (Oryza sativa sp. japonica), which showed good yields under flood irrigation conditions with 8,100 and 7,300 kg/ha for both seasons, respectively. However, under intermittent irrigation conditions, it showed a drastic yield reduction of 94 and 42% for both seasons, respectively. While the short-cycle variety MA042, showed yields of 12,663 and 11,063 kg/ha under flood irrigation strategy for both seasons, while under AWD system it showed only 34 and 4% reduction, respectively. The use of short cycle varieties, which require less water and are more tolerant to low temperatures, represent a productive option for rice farmers in cold climates, where water resources are an increasingly scarce commodity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".